This data set corresponds to the processing of data acquired by the British Antarctic Survey (BAS) airborne Synthetic Aperture Radar (SAR) PASIN2 (Polarimetric Airborne Scientific INstrument, mark 2), designed for deep ice sounding and basal 3D-mapping. The dataset includes the processed calibration data collected over the sea surface near Rothera Research Station during the Antarctic Summers campaigns in 2016/17 FISS (Filchner Ice Shelf System) and 2019/20 BEAMISH (Bed Access, Monitoring and Ice Sheet History) projects, and the processed SAR images as depth profiles in the Recovery Ice Stream near its grounding line, in 2016/17 (FISS). With multiple antennas for transmission and reception at 150-MHz central frequency, and an across-track physical array, PASIN2 resolves the ambiguities for distinguishing between scatterers from port and starboard directions. After processing several 2D SAR images (range and along-track dimensions) with transmitter-receiver pairs, the directional ambiguities are resolved, obtaining the across-track Direction of Arrival (DoA, elevation angle) estimation. Finally, from the 3D geometry of range, along-track and across-track angle, the real depths and across-track distances are estimated, regarding the case of the incorrectly assumed vertical DoA of a single SAR image. The calibration flights assessed and validated the instrument antenna patterns and processing performances. In this dataset, only the simulated and measured antenna patterns, and SAR and DoA images are included. By resolving directional ambiguities and accounting for reflector across-track location, the true ice thickness and bed elevation are obtained, thereby removing the error of the usual assumption of vertical DoA, that greatly influence the output of flow models of ice dynamics. This work was supported by NERC grant reference NE/L013444/1.

This dataset presents point annotations of stranded whale (Sperm whales, Physeter macrocephalus) and dolphin (Pilot whales, Globicephala melas edwardii) species identified in very high-resolution (VHR) optical and SAR satellite imagery, along offshore islands of New Zealand and Tasmania, between 2018-2023. Cetacean strandings offer significant conservation value for the assessment of ecosystems and serve as early warning of emerging concerns regarding animal, ocean, and human health. However stranding monitoring programmes are scarce or non-existent along minimally populated areas, coastlines with limited economic resources, geographically remote areas, complex coastlines and areas of geopolitical unrest. VHR satellite imagery offers the prospect of improving monitoring in these regions. While VHR satellite imagery is able to detect large baleen whale strandings, mass strandings are predominantly smaller-sized odontocetes (toothed whale and dolphin species). Detecting odontocetes is therefore crucial for VHR satellites to be useful for monitoring strandings globally. In addition, scaling up the use of VHR optical satellite imagery is limited by cloud cover, the primary environmental condition governing successful imagery collection. Synthetic Aperture Radar (SAR) satellites enable VHR imaging of Earth in cloudy regions and in darkness. This approach could facilitate strandings detection in cloudy regions and independent of daylight hours, which is critical for enabling timely emergency responses to unfolding stranding events. Here, we present data from four smaller odontocete mass strandings of long-finned pilot whale (LFPW), on Chatham, Pitt and Stewart Island, New Zealand, and one large odontocete (sperm whale) mass stranding on King Island, Tasmania, Australia between 2018-2023, to successfully detect and quantify large and small odontocete strandings in VHR optical and SAR satellite imagery. This research has been supported by the Natural Environment Research Council (NERC) through a SENSE CDT studentship (grant no. NE/T00939X/1). The research was further supported by additional funding provided through, the British Antarctic Survey (BAS) Innovation Voucher, Sentinel Hub and their #30MapChallenge competition, BAS Ecosystems, and the support and cooperation of Airbus and Maxar Technologies Ltd, for their rapid response and efforts to enable successful collection of the imagery analysed here.